Effect of deep cryogenic treatment on microstructure, mechanical properties and machining performances of coated carbide tool

Titanium alloys are widely utilized in aerospace thanks to their excellent combination of high-specific strength, fracture, corrosion resistance characteristics, etc. However, titanium alloys are difficult-to-machine materials. Tool wear is thus of great importance to understand and quantitatively predict tool life. In this study, the wear of coated carbide tool in milling Ti-6Al-4V alloy was assessed by characterization of the worn tool cutting edge. Furthermore, a tool wear model for end milling cutter is established with considering the joint effect of cutting speed and feed rate for characterizing tool wear process and predicting tool wear. Based on the proposed tool wear model equivalent tool life is put forward to evaluate cutting tool life under different cutting conditions. The modelling process of tool wear is given and discussed according to the specific conditions. Experimental work and validation are performed for coated carbide tool milling Ti-6Al-4V alloy.

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Effect of deep cryogenic treatment on mechanical properties and microstructure of Sn3.0Ag0.5Cu solder

Journal of Materials Science Materials in Electronics ◽

10.1007/s10854-017-8400-6 ◽

2017 ◽

Vol 29 (6) ◽

pp. 4517-4525 ◽

Cited By ~ 4

Author(s):

Yao Yao ◽

Xiao Li ◽

Xu He

Keyword(s):

Mechanical Properties ◽

Cryogenic Treatment ◽

Deep Cryogenic Treatment

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Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.1515/bpasts-2017-0060 ◽

2017 ◽

Vol 65 (4) ◽

pp. 553-559 ◽

Cited By ~ 1

Author(s):

D. Rajeev ◽

D. Dinakaran ◽

S.C.E. Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tool Wear ◽

Hard Turning ◽

Cutting Tool ◽

Carbide Tool ◽

Online Tool ◽

Coated Carbide Tool ◽

Tool Wear Estimation ◽

Coated Carbide

AbstractNowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.

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